One technique that has gained significant momentum is the use of machine learning (ML) to determine the photometric redshift of AGNs [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}, [10]}, [11]}, [4]}, [13]}, [5]}.
Large AGN data sets derived from all-sky surveys like the Wide-field Infrared Survey Explorer (WISE) [15]}, [16]}, [17]}, [1]}, [19]}, [20]} and Sloan Digital Sky Survey (SDSS) [21]} have played a significant role in the proliferation of ML approaches.
However, the quality of the results from an ML approach depends significantly on the size and quality of the training data: the data on which the ML models learn the underlying relationship to predict the redshift.
Unfortunately, almost all of these large data sets suffer from the issue of missing entries, which can lead to a considerable portion of the data being discarded.
